package ai.mlc

import android.content.Context
import android.graphics.Bitmap
import android.util.Log
import cn.ocars.playgame.pencilstudy.model.MiniCPMEngine
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.withContext
import java.nio.ByteBuffer

/**
 * MLC聊天模块的真实实现
 * 使用MiniCPM-V模型进行多模态推理
 */
class MLCChatModule private constructor(
    private val context: Context,
    private val modelPath: String,
    private val modelQuantization: String
) {
    
    private val TAG = "MLCChatModule"
    private var miniCPMEngine: MiniCPMEngine? = null
    private var isInitialized = false
    
    /**
     * 初始化模块
     */
    private suspend fun initialize() {
        if (isInitialized) return
        
        try {
            Log.d(TAG, "初始化MLC聊天模块...")
            
            // 创建MiniCPM引擎
            miniCPMEngine = MiniCPMEngine(context)
            
            // 初始化引擎
            val success = miniCPMEngine?.initialize(modelPath, modelQuantization) ?: false
            
            if (success) {
                isInitialized = true
                Log.d(TAG, "MLC聊天模块初始化成功")
            } else {
                Log.e(TAG, "MLC聊天模块初始化失败")
            }
        } catch (e: Exception) {
            Log.e(TAG, "MLC聊天模块初始化失败", e)
        }
    }
    
    /**
     * 生成响应
     * @param prompt 提示词
     * @param image 图像数据
     * @return 生成的响应
     */
    suspend fun generate(prompt: String, image: ByteBuffer): String? = withContext(Dispatchers.IO) {
        if (!isInitialized) {
            Log.w(TAG, "模块未初始化，尝试初始化...")
            initialize()
        }
        
        if (!isInitialized) {
            Log.e(TAG, "模块初始化失败，无法生成响应")
            return@withContext null
        }
        
        try {
            Log.d(TAG, "正在生成响应...")
            
            // 使用MiniCPM引擎生成响应
            val response = miniCPMEngine?.generateScore(prompt, image)
            
            Log.d(TAG, "响应生成成功")
            response
        } catch (e: Exception) {
            Log.e(TAG, "生成响应失败", e)
            null
        }
    }
    
    /**
     * 生成响应（使用Bitmap）
     * @param prompt 提示词
     * @param bitmap 图像位图
     * @return 生成的响应
     */
    suspend fun generate(prompt: String, bitmap: Bitmap): String? = withContext(Dispatchers.IO) {
        if (!isInitialized) {
            Log.w(TAG, "模块未初始化，尝试初始化...")
            initialize()
        }
        
        if (!isInitialized) {
            Log.e(TAG, "模块初始化失败，无法生成响应")
            return@withContext null
        }
        
        try {
            Log.d(TAG, "正在生成响应...")
            
            // 使用MiniCPM引擎生成响应
            val response = miniCPMEngine?.generateScore(prompt, bitmap)
            
            Log.d(TAG, "响应生成成功")
            response
        } catch (e: Exception) {
            Log.e(TAG, "生成响应失败", e)
            null
        }
    }
    
    /**
     * 加载模型
     */
    suspend fun load() {
        if (!isInitialized) {
            initialize()
        }
        
        if (isInitialized) {
            Log.d(TAG, "模型已加载")
        } else {
            Log.e(TAG, "模型加载失败")
        }
    }
    
    /**
     * 卸载模型
     */
    fun unload() {
        if (isInitialized) {
            miniCPMEngine?.release()
            miniCPMEngine = null
            isInitialized = false
            Log.d(TAG, "模型已卸载")
        }
    }
    
    /**
     * 重置模型
     */
    fun reset() {
        if (isInitialized) {
            miniCPMEngine?.reset()
            Log.d(TAG, "模型已重置")
        }
    }
    
    /**
     * 检查模型是否已加载
     */
    fun isModelLoaded(): Boolean {
        return isInitialized && (miniCPMEngine?.isModelLoaded() ?: false)
    }
    
    /**
     * 构建器类
     */
    class Builder {
        private var context: Context? = null
        private var modelPath: String = "minicpm-v-2.5"
        private var modelQuantization: String = "q4f16_0"
        
        /**
         * 设置上下文
         */
        fun setContext(context: Context): Builder {
            this.context = context
            return this
        }
        
        /**
         * 设置模型路径
         */
        fun setModelPath(modelPath: String): Builder {
            this.modelPath = modelPath
            return this
        }
        
        /**
         * 设置模型量化方式
         */
        fun setModelQuantization(modelQuantization: String): Builder {
            this.modelQuantization = modelQuantization
            return this
        }
        
        /**
         * 构建MLCChatModule实例
         */
        fun build(): MLCChatModule {
            val context = this.context
                ?: throw IllegalStateException("Context must be set")
            
            return MLCChatModule(context, modelPath, modelQuantization)
        }
    }
}